1131 Words5 Pages

PROJECT PART C: Regression and Correlation Analysis

Math-533 Applied Managerial Statistics

Prof. Jeffrey Frakes

December 12, 2014

Jared D Stock

1. Generate a scatterplot for income ($1,000) versus credit balance ($), including the graph of the best fit line. Interpret.

This scatter plot graph is a representation of combining income and credit balance. It shows the income increasing as the credit balance increases. As a result of this data it can be inferred that there is a positive relationship between the two variables. Because of the positive relationship between income and credit balance the best fit line or linear regression line fits the data quite well. The speculation can be strongly made that the*…show more content…*

A customer with a $10,000 credit balance is, more than likely, going to have an income of $115,748.27. That is according to the fitted regression model.

In an attempt to improve the model, we attempt to do a multiple regression model predicting income based on credit balance, years, and size.

11. Using MINITAB, run the multiple regression analysis using the variables credit balance, years, and size to predict income. State the equation for this multiple regression model.

Regression Analysis: Income($1000) versus Credit Balance($), Size, Years

The regression equation is

Income($1000) = - 13.2 + 0.0108 Credit Balance($) + 0.615 Size + 1.21 Years

Predictor Coef SE Coef T P

Constant -13.186 3.608 -3.65 0.001

Credit Balance($) 0.0107922 0.0008184 13.19 0.000

Size 0.6151 0.4178 1.47 0.148

Years 1.2097 0.2322 5.21 0.000

S = 5.26121 R-Sq = 86.5% R-Sq(adj) = 85.6%

Analysis of Variance

Source DF SS MS F P

Regression 3 8171.7 2723.9 98.41 0.000

Residual Error 46 1273.3 27.7

Total 49 9445.0

Source DF Seq SS

Credit Balance($) 1 6052.7

Size 1 1368.0

Years 1 750.9

The fitted regression line:

Income = -13.186 +0.0107922* Credit Balance + 0.6151* Size + 1.2097*Years.

12.

Math-533 Applied Managerial Statistics

Prof. Jeffrey Frakes

December 12, 2014

Jared D Stock

1. Generate a scatterplot for income ($1,000) versus credit balance ($), including the graph of the best fit line. Interpret.

This scatter plot graph is a representation of combining income and credit balance. It shows the income increasing as the credit balance increases. As a result of this data it can be inferred that there is a positive relationship between the two variables. Because of the positive relationship between income and credit balance the best fit line or linear regression line fits the data quite well. The speculation can be strongly made that the

A customer with a $10,000 credit balance is, more than likely, going to have an income of $115,748.27. That is according to the fitted regression model.

In an attempt to improve the model, we attempt to do a multiple regression model predicting income based on credit balance, years, and size.

11. Using MINITAB, run the multiple regression analysis using the variables credit balance, years, and size to predict income. State the equation for this multiple regression model.

Regression Analysis: Income($1000) versus Credit Balance($), Size, Years

The regression equation is

Income($1000) = - 13.2 + 0.0108 Credit Balance($) + 0.615 Size + 1.21 Years

Predictor Coef SE Coef T P

Constant -13.186 3.608 -3.65 0.001

Credit Balance($) 0.0107922 0.0008184 13.19 0.000

Size 0.6151 0.4178 1.47 0.148

Years 1.2097 0.2322 5.21 0.000

S = 5.26121 R-Sq = 86.5% R-Sq(adj) = 85.6%

Analysis of Variance

Source DF SS MS F P

Regression 3 8171.7 2723.9 98.41 0.000

Residual Error 46 1273.3 27.7

Total 49 9445.0

Source DF Seq SS

Credit Balance($) 1 6052.7

Size 1 1368.0

Years 1 750.9

The fitted regression line:

Income = -13.186 +0.0107922* Credit Balance + 0.6151* Size + 1.2097*Years.

12.

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